Medical Imaging

Physicians utilize medical imaging to see inside the body to diagnose and treat patients. This includes computed tomography (CT), magnetic resonance imaging (MRI), X-ray, ultrasound, fluoroscopy, angiography,  and the nuclear imaging modalities of PET and SPECT. 

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Technologies combine to detect, localize and visualize pain signals in the brain

Aided by augmented reality, AI and portable neuroimaging technology, physicians may soon be able to tease out images of patients’ brains—right there in the doctor’s office—to see how much pain each patient is suffering.

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How Secure Is That Scanner?

In a world of networked medical devices, it’s not hard to imagine a radiology-heavy cyberattack that is not only malicious but also ingenious.
 

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Algorithm unmasks bugs bearing diseases

Health officials south of the border may soon be able to fight a nasty disease using just their smartphones and an AI tool for reverse image searches.

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AI in radiology: There’ll be an ‘app store’ for that

Radiology is the medical specialty most conducive to clinical AI applications. After all, the pre-AI technique of computer-aided detection has been used in mammography since 1998, for example. So it shouldn’t come as a surprise to find AI “app stores” rising in radiology.

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Deep-learning tool smartly steers patients toward—or away from—breast biopsy

Researchers in Texas and Taiwan have collaborated to develop a deep-learning tool that can precisely asses the risk of breast cancer—and with it the need for biopsy—in patients with lesions of questionable concern found in mammograms.

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French radiologists behind on AI but eager to learn more

Radiology, the medical specialty into which AI has made the furthest initial inroads in the U.S., is embracing the technology in France. And this is so despite French radiologists feeling underinformed on AI up to now.

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AI helps ID schizophrenia through brain imaging

Researchers have identified an approach for more accurately diagnosing schizophrenia using AI, bringing some objectivity to the field of neurodevelopmental disorder diagnosis, according to a study published in the May edition of Artificial Intelligence in Medicine.

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Chi-square risk estimation helps reduce noise in MR images

A combination of the established denoising algorithm NeighShrink and chi-square unbiased risk estimation (CURE) could reduce noise in magnetic resonance (MR) images more effectively than traditional methods, according to research published in Artificial Intelligence in Medicine.

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